Researcher Argues Coding Is ML's Real Bottleneck — and Coding Agents Are Fixing It
peterjliu · x · 2026-09-03
Responding to a thread asking whether we're in the "early innings of RSI," peterjliu argues the primary bottleneck in ML research is actually coding implementation, running experiments, organizing and interpreting results — all of which are now much easier with coding agents. The take frames coding agents, not raw model capability, as the key accelerant of research velocity.
More from coding & agent
- Muse Spark 1.3 released with focus on instruction following and long-horizon coding — armand_ruiz · 2026-09-03
- GitHub decodes the new AI dev lingo: loop engineering, harnesses, squads and hill climbing — GitHub Blog AI/ML · 2026-09-03
- Reef: open-source infra that lets agents keep evolving weights and harness after deployment — Scobleizer · 2026-09-03
- Self-Hosted Agent Memory Failed Silently: Writes Fine, Retrieval Dead, No Errors — eldrugo85 · 2026-09-03
- Thoughtworks CTO: maybe we shouldn't be reviewing all this code anymore — rseroter · 2026-09-03
- moyix says XBOW can also pull off full-chain autonomous pentesting — moyix · 2026-09-03